sdf-xarray

sdf-xarray provides a backend for xarray to read SDF files as created by the EPOCH plasma PIC code using the SDF-C library.

Installation

Install from PyPI with:

pip install sdf-xarray

[!NOTE] For use within jupyter notebooks, run this additional command after installation:

pip install "sdf-xarray[jupyter]"

or from a local checkout:

git clone https://github.com/PlasmaFAIR/sdf-xarray.git
cd sdf-xarray
pip install .

We recommend switching to uv to manage packages.

Usage

For more in depth documentation please visit https://sdf-xarray.readthedocs.io/

sdf-xarray is a backend for xarray, and so is usable directly from xarray:

Single file loading

import xarray as xr

df = xr.open_dataset("0010.sdf")

print(df["Electric_Field_Ex"])

# <xarray.DataArray 'Electric_Field_Ex' (X_x_px_deltaf_electron_beam: 16)> Size: 128B
# [16 values with dtype=float64]
# Coordinates:
#   * X_x_px_deltaf_electron_beam  (X_x_px_deltaf_electron_beam) float64 128B 1...
# Attributes:
#     units:    V/m
#     full_name: "Electric Field/Ex"

To open a whole simulation at once, pass preprocess=sdf_xarray.SDFPreprocess() to xarray.open_mfdataset:

import xarray as xr
from sdf_xarray import SDFPreprocess

with xr.open_mfdataset("*.sdf", preprocess=SDFPreprocess()) as ds:
    print(ds)

# Dimensions:
# time: 301, X_Grid_mid: 128, ...
# Coordinates: (9) ...
# Data variables: (18) ...
# Indexes: (9) ...
# Attributes: (22) ...

SDFPreprocess checks that all the files are from the same simulation, as ensures there's a time dimension so the files are correctly concatenated.

If your simulation has multiple output blocks so that not all variables are output at every time step, then those variables will have NaN values at the corresponding time points.

Alternatively, we can create a separate time dimensions for each output block (essentially) using sdf_xarray.open_mfdataset with separate_times=True:

from sdf_xarray import open_mfdataset

with open_mfdataset("*.sdf", separate_times=True) as ds:
    print(ds)

# Dimensions:
# time0: 301, time1: 31, time2: 61, X_Grid_mid: 128, ...
# Coordinates: (12) ...
# Data variables: (18) ...
# Indexes: (9) ...
# Attributes: (22) ...

This is better for memory consumption, at the cost of perhaps slightly less friendly comparisons between variables on different time coordinates.

Reading particle data

By default, particle data isn't kept as it takes up a lot of space. Pass keep_particles=True as a keyword argument to open_dataset (for single files) or open_mfdataset (for multiple files):

df = xr.open_dataset("0010.sdf", keep_particles=True)

Loading SDF files directly

For debugging, sometimes it's useful to see the raw SDF files:

from sdf_xarray import SDFFile

with SDFFile("0010.sdf") as sdf_file:
    print(sdf_file.variables["Electric Field/Ex"])

    # Variable(_id='ex', name='Electric Field/Ex', dtype=dtype('float64'), ...

    print(sdf_file.variables["Electric Field/Ex"].data)

    # [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ... -4.44992788e+12  1.91704994e+13  0.00000000e+00]